How to access pandas groupby dataframe by key

You can use the get_group method:

In [21]: gb.get_group('foo')Out[21]:      A         B   C0  foo  1.624345   52  foo -0.528172  114  foo  0.865408  14

Note: This doesn't require creating an intermediary dictionary / copy of every subdataframe for every group, so will be much more memory-efficient than creating the naive dictionary with dict(iter(gb)). This is because it uses data-structures already available in the groupby object.

You can select different columns using the groupby slicing:

In [22]: gb[["A", "B"]].get_group("foo")Out[22]:     A         B0  foo  1.6243452  foo -0.5281724  foo  0.865408In [23]: gb["C"].get_group("foo")Out[23]:0     52    114    14Name: C, dtype: int64

Wes McKinney (pandas' author) in Python for Data Analysis provides the following recipe:

groups = dict(list(gb))

which returns a dictionary whose keys are your group labels and whose values are DataFrames, i.e.

groups['foo']

will yield what you are looking for:

     A         B   C0  foo  1.624345   52  foo -0.528172  114  foo  0.865408  14

Rather than

gb.get_group('foo')

I prefer using gb.groups

df.loc[gb.groups['foo']]

Because in this way you can choose multiple columns as well. for example:

df.loc[gb.groups['foo'],('A','B')]